Integration of geoscientific uncertainty into geophysical inversion  by means of local gradient regularization

Giraud, Jeremie; Lindsay, Mark; Ogarko, Vitaliy; Jessell, Mark; Martin, Roland; Pakyuz-Charrier, Evren

doi:https://doi.org/10.5194/se-10-193-2019

Articles | Volume 10, issue 1

https://doi.org/10.5194/se-10-193-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/se-10-193-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 10, issue 1

Research article

|

25 Jan 2019

Research article |

| 25 Jan 2019

Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization

Jeremie Giraud, Mark Lindsay, Vitaliy Ogarko, Mark Jessell, Roland Martin, and Evren Pakyuz-Charrier

Data sets

Yerrida Basin Geophysical Modeling -- Input data and inverted models, Version version 1.0 J. Giraud, M. Lindsay, and V. Ogarko https://doi.org/10.5281/zenodo.1238216

Synthetic dataset for the testing of local conditioning of regularization function using geological uncertainty, Version version 1.0 J. Giraud, V. Ogarko, and V. Pakyuz-Charrier https://doi.org/10.5281/zenodo.1238529

Short summary

We propose the quantitative integration of geology and geophysics in an algorithm integrating the probability of observation of rocks with gravity data to improve subsurface imaging. This allows geophysical modelling to adjust models preferentially in the least certain areas while honouring geological information and geophysical data. We validate our algorithm using an idealized case and apply it to the Yerrida Basin (Australia), where we can recover the geometry of buried greenstone belts.